This advanced course is intended for researchers working on empirical and quantitative questions. During the training, the participants will have the opportunity to test and consolidate what they have learned with practical exercises, so that they can later work independently with the learned statistical methods.
Rationale of the course
This course is based on the course "Introduction to R", gives a comprehensive overview of the field of statistics and teaches the participants the most important methods of statistical analysis in R. Participants will be introduced to analyses from three thematic blocks: multivariate statistics, time series analysis and data mining.
Aim of the course
Participants will learn how to use methods of multivariate statistics to uncover patterns and relationships in data. Therefore, the course contains an introduction to time series analysis and central smoothing and forecasting methods as well as an overview of machine learning algorithms and a practical presentation of the typical workflow of a machine learning project in R.
- Introduction to multivariate statistical methods
- Central smoothing and forecasting methods of time series analyses
- Presentation of typical data minig algorithms
- Demonstration and interpretation of different data mining metrics for performance measurement
Trainer - Martin Schneider
Since 2012, Martin Schneider has been working as a Senior Data Scientist at eoda GmbH. His work focuses on data analysis projects and knowledge transfer of data science. He has 8 years of experience in different data science projects as data scientist and project manager as well as in trainings and workshops as moderator and certified trainer.
- 22 July 2021, 09:30 – 12:30 h + 14:00 – 17:00 h
- 23 July 2021, 09:30 – 12:30 h + 14:00 – 17:00 h
Please register viea the GRS Virtual Campus. You can find this event in »Area 3: Scientific Techniques«: www.b-tu.de/elearning/graduates
Bemerkung zum Termin:
Die Veranstaltung findet in englischer Sprache statt.
Nach Ihrer Anmeldung erhalten Sie den Link ca. eine Woche vor Veranstaltungsbeginn.
ZE Graduate Research School (GRS)
T +49 (0) 355 69-3479